在PEMFC设计中,集成的神经网络和元启发算法用于平衡电性能和热安全
Naim Ben Ali1, Borhen Louhichi2, As'ad Alizadeh3
1Department of Industrial Engineering, College of Engineering, University of Ha'il, Ha'il City, 81451, Saudi Arabia.
Scientific reports
|November 29, 2025
概括
本研究介绍了对质子交换膜燃料电池 (PEMFCs) 的预测-优化-决策框架. 综合方法提高了电力输出和热稳定性,以实现高效的PEMFC设计.
科学领域:
- 能源系统工程 能源系统工程
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 质子交换膜燃料电池 (PEMFCs) 需要优化设计,以实现高电力输出和热稳定性.
- 操作参数之间的复杂相互作用使PEMFC设计和性能复杂化.
- 现有的方法难以平衡竞争对手的目标,例如输出功率和热管理.
研究的目的:
- 为PEMFCs开发一个综合的预测-优化-决策框架.
- 系统地建模PEMFC性能,并探索电力输出和热稳定性之间的权衡.
- 引导特定应用的设计选择,以提高PEMFC的效率和安全性.
主要方法:
- 集成多层感知神经网络 (MLPNN) 用于预测建模.
- 应用元启发式优化算法:粒子群优化 (PSO),修改PSO (MPSO),多目标哈里斯霍克斯优化 (MOHHO) 和多目标PSO (MOPSO).
- 使用添加比率评估 (ARAS) 方法来选择最佳设计场景的决策.
主要成果:
- 在预测电力输出 (MAPE=0.233%) 和电池温度 (MAPE=0.301%) 方面,PSO-MLPNN和MPSO-MLPNN分别表现出高准确度.
- 多目标优化揭示了功率和温度之间的权衡,MOHHO产生了优越的帕雷托前线.
- 最佳运行条件实现了接近5300mW的峰值功率输出,电池温度稳定在39.5°C左右.
- 通过ARAS方法确定了设计场景,均衡权重场景比数据集平均值增加了6.94%的输出功率,降低了20.3%的温度.
结论:
- 拟议的框架有效地平衡了PEMFC中的电性能和热稳定性.
- 它提供灵活的,针对特定应用的设计策略,以提高效率和安全性.
- 综合方法提供了一种系统的方法来优化复杂的能源系统.
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